Various systems exist to create and store geospatial data. For example, satellites orbiting earth can be equipped with cameras that capture images of the earth. In another example, high-resolution cameras can be coupled to aircraft (drones and/or manned aircraft), and images captured by these high-resolution cameras can be assigned geographic data. Moreover, synthetic aperture radar (SAR) technologies can be utilized in connection with generating geospatial data. If a region is monitored over time, geospatial temporal data can be generated. Conventionally, however, there is a lack of suitable technologies for analyzing such data (and similar types of temporal data).
Volatility measures are used in some industries to allow analysts to identify outlier conditions in time-series data. Certain volatility measures have been used to trigger a responsive action (where the action is undertaken when some feature of the time-series data exceeds or falls beneath a threshold). These threshold values can be calculated in part based upon rules of thumb that define values of various volatility measure parameters, such as a period for calculating a moving average of pricing data for the asset and a number of standard deviations away from the moving average the threshold should be. These rules of thumb are generally data-independent, and may place arbitrary constraints on the volatility analysis problem.